Similarity Measures for Short Segments of Text

  • Metzler D
  • Dumais S
  • Meek C
  • 314

    Readers

    Mendeley users who have this article in their library.
  • 125

    Citations

    Citations of this article.

Abstract

Measuring the similarity between documents and queries has been extensively studied in information retrieval. However, there are a growing number of tasks that require computing the similarity between two very short segments of text. These tasks include query reformulation, sponsored search, and image retrieval. Standard text similarity measures perform poorly on such tasks because of data sparseness and the lack of context. In this work, we study this problem from an information retrieval perspective, focusing on text representations and similarity measures. We examine a range of similarity measures, including purely lexical measures, stemming, and language modeling-based measures. We formally evaluate and analyze the methods on a query-query similarity task using 363,822 queries from a web search log. Our analysis provides insights into the strengths and weaknesses of each method, including important tradeoffs between effectiveness and efficiency.

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Get full text

Authors

  • Donald Metzler

  • Susan Dumais

  • Christopher Meek

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free